The groupby method allows you to group rows of data together and call aggregate functions
import pandas as pd
# Create dataframe
data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
'Sales':[200,120,340,124,243,350]}
df = pd.DataFrame(data)
df
Now you can use the .groupby() method to group rows together based off of a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:
df.groupby('Company')
You can save this object as a new variable:
by_comp = df.groupby("Company")
And then call aggregate methods off the object:
by_comp.mean()
df.groupby('Company').mean()
More examples of aggregate methods:
by_comp.std()
by_comp.min()
by_comp.max()
by_comp.count()
#Give you summary of data
by_comp.describe()
#Will give you summary
by_comp.describe().transpose()
#Selec only one column of summary
by_comp.describe().transpose()['GOOG']
See you on next lecture..